Intra-aortic pressure forecasting

ABSTRACT

Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/889,457, filed Jun. 1, 2020, now U.S. Pat. No. 11,581,083 B2, whichclaims the benefit of the U.S. Provisional Application No. 62/855,389,filed May 31, 2019, all of which are incorporated herein by reference.

TECHNICAL FIELD

The present technology relates to systems and methods for predicting anintra-aortic pressure of a patient receiving hemodynamic support from atransvalvular micro-axial heart pump.

BACKGROUND

Machine learning has been successfully applied in a variety of differenttechnical fields, such as computer vision, natural language processing,speech recognition, and clinical healthcare, to provide predictions.Examples of machine learning algorithms include Bayesian algorithms,clustering algorithms, decision tree algorithms, dimensionalityreduction algorithms, instance-based algorithms, deep learningalgorithms, regression algorithms, regularization algorithms, andrule-based machine learning algorithms. In clinical healthcare, machinelearning algorithms have been used for modeling risk of mortality,forecasting length of stay, detecting physiologic decline, andclassifying phenotypes. See, e.g., Harutyunyan et al., Multitasklearning and benchmarking with clinical time series data, ScientificData, doi: 10.1038/s41597-019-0103-9, 2017; Purushothama et al.,Benchmarking deep learning models on large healthcare datasets, Journalof Biomedical Informatics 83, 112-134, 2018. However, there remains aneed for systems and methods for predicting physiological responses,which could help physicians with real-time early detection of diseasesand patient response to therapies.

BRIEF SUMMARY

Heretofore, machine learning algorithms have not been used to predict anintra-aortic pressure (e.g., current intra-aortic pressure, meanintra-aortic pressure, median intra-aortic pressure, maximumintra-aortic pressure, minimum intra-aortic pressure, range ofintra-aortic pressure, intra-aortic pressure during systole,intra-aortic pressure during diastole, etc.) of a patient receivinghemodynamic support. Forecasting the intra-aortic pressure of a patientis challenging, in part, because a high frequency intra-aortic bloodpressure time series is not currently publicly available. Furthermore,an intra-aortic blood pressure time series can be noisy and highlynon-stationary. Moreover, forecasting error and uncertainty growsdrastically for long-term forecasting.

The ability to predict an intra-aortic pressure of a patient wouldgreatly enhance the ability of clinicians to forecast the condition ofthe patient. For example, acute decompensated heart failure (ADHF) is acomplex clinical event associated with excess morbidity and mortality,which is generally indicated by a rapid decline in blood pressure,associated with an increase in heart rate. The challenge of ADHF is thelack of effective treatments that both reduce symptoms and improveclinical outcomes. Existing guideline recommendations are largely basedon expert opinion. See, e.g., Givertz et al., Acute Decompensated HeartFailure: Update on New and Emerging Evidence and Directions for FutureResearch, Journal of Cardiac Failure, Vol. 19, No. 6, 2013. Thus, beingable to predict the trajectory of an intra-aortic pressure of a patientwould make it easier for medical practitioners to evaluate the patient'srisk of ADHF and intervene prior to collapse. In addition, intra-aorticpressure forecasting would provide helpful guidance for weaning patientsoff support as their health improves.

Aspects of the present disclosure describe systems and methods forpredicting an intra-aortic pressure of a patient receiving hemodynamicsupport from a transvalvular micro-axial heart pump. In someimplementations, an intra-aortic pressure time series is derived frommeasurements of a pressure sensor of the transvalvular micro-axial heartpump and a motor speed time series is derived from a measured backelectromotive force (EMF) of a motor of the transvalvular micro-axialheart pump. Furthermore, in some implementations, machine learningalgorithms, such as deep learning, are applied to the intra-aorticpressure and motor speed time series to accurately predict anintra-aortic pressure of the patient. In some implementations, theprediction is short-term (e.g., approximately 5 minutes in advance).

One aspect of the present disclosure relates to a system including atransvalvular micro-axial heart pump and one or more processors. Thetransvalvular micro-axial heart pump includes a motor and a pressuresensor. The one or more processors are configured to: obtain a set ofintra-aortic pressure measurements corresponding to pressure valuesmeasured by the pressure sensor during a period of time when thetransvalvular micro-axial pump is at least partially located in apatient's heart, obtain a set of motor speed measurements correspondingto rotational speeds of the motor during the period of time, predict,using a trained machine learning model, an intra-aortic pressure of apatient based on the sets of intra-aortic pressure and motor speedmeasurements, and automatically adjust a speed setting of the motorbased on the predicted intra-aortic pressure of the patient.

In some implementations, the one or more processors are furtherconfigured to obtain a set of current measurements corresponding to anenergy intake of the motor during the period of time, and the predictionis further based on the set of current measurements.

In some implementations, the transvalvular micro-axial heart pumpfurther includes a tube, an inlet area having one or more openingsthrough which blood may be drawn into the tube by the motor, and anoutlet area having one or more openings through which blood may beexpelled from the tube by the motor, and the pressure sensor is coupledto the outlet area. In some implementations, the transvalvularmicro-axial heart pump further includes an additional pressure sensorcoupled to the inlet area, the one or more processors are furtherconfigured to obtain a set of left ventricular pressure measurementscorresponding to pressure values measured by the additional pressuresensor during the period of time, and the prediction is further based onthe set of left ventricular pressure measurements.

In some implementations, the machine learning model is a deep learningmodel. In some implementations, the deep learning model is anAutoregressive Integrated Moving Average (ARIMA) model, a Deep NeuralNetwork (DNN) model, a Recurrent Sequence to Sequence model, a RecurrentSequence to Sequence model with Attention, a Transformer model, aTemporal Convolutional Neural Network (TCN) model, or a ConvolutionalNeural Pyramid model. In some implementations, the deep learning modelis a Recurrent Sequence to Sequence model with a Legendre Memory Unit(LMU).

In some implementations, the machine learning model is trained on a dataset having increasing sequences, decreasing sequences, and stationarysequences, wherein each sequence includes intra-aortic pressure andmotor speed measurements. In some implementations, a sequence isincreasing if the intra-aortic pressure measurements within thatsequence increase by more than a predetermined threshold, a sequence isdecreasing if the intra-aortic pressure measurements within thatsequence decrease by more than the predetermined threshold, and asequence is stationary if the intra-aortic pressure measurements withinthat sequence do not increase or decrease by more than the predeterminedthreshold. In some implementations, the predetermined threshold is 10mmHg. In some implementations, each sequence includes a predeterminednumber of aortic pressure and motor speed measurements. In someimplementations, each sequence includes real-time (RT) intra-aorticpressure and motor speed measurements. In some implementations, eachsequence includes average time (AT) intra-aortic pressure and motorspeed measurements.

In some implementations, the machine learning model is trained on a dataset having only increasing and decreasing sequences, wherein eachsequence includes intra-aortic pressure and motor speed measurements. Insome implementations, a sequence is increasing if the intra-aorticpressure measurements within that sequence increase by more than apredetermined threshold, and a sequence is decreasing if theintra-aortic pressure measurements within that sequence decrease by morethan the predetermined threshold. In some implementations, thepredetermined threshold is 10 mmHg. In some implementations, eachsequence includes a predetermined number of aortic pressure and motorspeed measurements. In some implementations, each sequence includesreal-time (RT) intra-aortic pressure and motor speed measurements. Insome implementations, each sequence includes average time (AT)intra-aortic pressure and motor speed measurements.

In some implementations, automatically adjusting the speed setting ofthe motor based on the predicted intra-aortic pressure of the patientincludes temporarily increasing the speed setting of the motor when thepredicted intra-aortic pressure of the patient is less than a currentintra-aortic pressure of the patient by more than a predeterminedamount.

Another aspect of the present disclosure relates to a system including atransvalvular micro-axial heart pump, one or more processors, and adisplay. The transvalvular micro-axial heart pump includes a motor and apressure sensor. The one or more processors are configured to: obtain aset of intra-aortic pressure measurements corresponding to pressurevalues measured by the pressure sensor during a period of time when thetransvalvular micro-axial pump is at least partially located in apatient's heart, obtain a set of motor speed measurements correspondingto rotational speeds of the motor during the period of time, andpredict, using a trained machine learning model, an intra-aorticpressure of the patient based on the sets of intra-aortic pressure andmotor speed measurements. The display is configured to display thepredicted intra-aortic pressure of the patient.

In some implementations, the display is configured to simultaneouslydisplay the predicted intra-aortic pressure of the patient with acurrent intra-aortic pressure of the patient and a current speed settingof the motor. In some implementations, the display is further configuredto display an alert when the predicted intra-aortic pressure of thepatient is less than a current intra-aortic pressure of the patient bymore than a predetermined amount. In some implementations, the displayis configured to display the predicted intra-aortic pressure of thepatient as part of a graph.

Yet another aspect of the present disclosure relates to a method fortreating a patient with a transvalvular micro-axial heart pump receivedinto the patient's body. The method includes: inserting a transvalvularmicro-axial heart pump into the body of a patient, obtaining a set ofintra-aortic pressure measurements corresponding to pressure valuesmeasured by a pressure sensor located on the transvalvular micro-axialheart pump during a period of time when the transvalvular micro-axialpump is at least partially located in the patient's heart, obtaining aset of motor speed measurements corresponding to rotational speeds ofthe motor during the period of time, predicting, using a trained machinelearning model, an intra-aortic pressure of the patient based on thesets of intra-aortic pressure and motor speed measurements, andautomatically adjusting a speed setting of the motor based on thepredicted intra-aortic pressure of the patient.

In some implementations, the method further includes obtaining a set ofcurrent measurements corresponding to an energy intake of the motorduring the period of time, and the prediction is further based on theset of current measurements.

In some implementations, the transvalvular micro-axial heart pumpfurther includes a tube, an inlet area having one or more openingsthrough which blood may be drawn into the tube by the motor, and anoutlet area having one or more openings through which blood may beexpelled from the tube by the motor, and the pressure sensor is coupledto the outlet area. In some implementations, the transvalvularmicro-axial heart pump further includes an additional pressure sensorcoupled to the inlet area, the method further includes obtaining a setof left ventricular pressure measurements corresponding to pressurevalues measured by the additional pressure sensor during the period oftime, and the prediction is further based on the set of left ventricularpressure measurements.

In some implementations, the method further includes adjusting an amountof a medication provided to the patient based on the predictedintra-aortic pressure. In some implementations, the method furtherincludes decreasing the motor speed if the intra-aortic pressure ispredicted to increase. In some implementations, the method furtherincludes increasing the motor speed if the intra-aortic pressure ispredicted to decrease.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1(a) illustrates a transvalvular micro-axial heart pump.

FIG. 1(b) illustrates the transvalvular micro-axial heart pump of FIG.1(a) positioned within the heart of a patient.

FIG. 1(c) illustrates a ventricular support system.

FIG. 2(a) illustrates information that may be displayed on a homescreen.

FIG. 2(b) illustrates information that may be displayed on a placementscreen.

FIG. 2(c) illustrates information that may be displayed on a purgescreen.

FIG. 2(d) illustrates information that may be displayed on an infusionhistory screen.

FIG. 2(e) illustrates information that may be displayed on a homescreen.

FIG. 2(f) illustrates information that may be displayed on a placementscreen.

FIG. 2(g) illustrates information that may be displayed on a placementscreen.

FIG. 2(h) illustrates information that may be displayed on a placementscreen.

FIG. 3 illustrates a system for monitoring and/or controlling aplurality of medical devices, such as transvalvular micro-axial heartpumps.

FIG. 4 illustrates the isovolumic relaxation phase, the ejection phase,the isovolumic relaxation phase, and the filling phase of a cardiaccycle.

FIG. 5 illustrates the regular waveforms of Intra-Aortic Pressure (AoP),Left Ventricular Pressure (LVP), Differential Pressure (dP), Pump Flow,and Motor Current, as well as their relationships with systole anddiastole.

FIG. 6 illustrates the overall structure of a recurrent sequence tosequence model.

FIG. 7 illustrates the overall structure of a transformer model.

FIG. 8 illustrates the overall structure of a temporal convolutionalneural network.

FIG. 9 illustrates the overall structure of a temporal convolutionalneural pyramid.

FIG. 10 illustrates 10-second 25 HZ(RT) intra-aortic pressure, motorspeed and motor current time series.

FIG. 11 illustrates a 20-minute 0.1 HZ(AT) mean intra-aortic pressuretime series.

FIG. 12 illustrates the root-mean-square error of select models.

FIG. 13 illustrates the MAP forecasts of two deep learning modelsagainst the ground truth for a single recording over the course of 24hours.

FIG. 14 illustrates the MAP forecasts of two deep learning models onincreasing sequences, decreasing sequences and stationary sequences.FIG. 14 is illustrated in two parts as FIGS. 14A and 14B.

FIG. 15 illustrates the root-mean-square error of select models. FIG. 15is illustrated in two parts as FIGS. 15A and 15B.

DETAILED DESCRIPTION

Implementations of the present disclosure are described in detail withreference to the drawing figures wherein like reference numeralsidentify similar or identical elements. It is to be understood that thedisclosed implementations are merely examples of the disclosure, whichmay be embodied in various forms. Well-known functions or constructionsare not described in detail to avoid obscuring the present disclosure inunnecessary detail. Therefore, specific structural and functionaldetails disclosed herein are not to be interpreted as limiting, butmerely as a basis for the claims and as a representative basis forteaching one skilled in the art to variously employ the presentdisclosure in virtually any appropriately detailed structure.

Efforts have been made to predict the peripheral blood pressure ofpatients with various machine learning models and statistical methods.See, e.g., Abbasi et al., Long-term Prediction of Blood Pressure TimeSeries Using Multiple Fuzzy Functions, 21st Iranian Conference onBiomedical Engineering, ICBME, 2014; Peng et al., Long-term BloodPressure Prediction with Deep Recurrent Neural Networks, arXiv:1705.04524v3, 2018.

Efforts have been made to predict whether patients are likely toexperience an acute hypotensive episode (AHE) with various machinelearning models and statistical methods. See, e.g., Henriques & Rocha,Prediction of Acute Hypotensive Episodes Using Neural NetworkMulti-models, Computers in Cardiology 36:549552, 2009; Moody & Lehman,Predicting Acute Hypotensive Episodes: The 10th AnnualPhysioNet/Computers in Cardiology Challenge, Comput. Cardiol.,36(5445351): 541-544, 2009; Johnson et al., MIMIC-III, a freelyaccessible critical care database, Scientific Data, DOI:10.1038/sdata.2016.35, 2016; Hatib et al., Machine-learning Algorithm toPredict Hypotension Based on High-Fidelity Arterial Pressure WaveformAnalysis, Anesthesiology, 129(4):663-674, 2018.

Efforts have been made to predict acute decompensated heart failure(ADHF) with various machine learning models and statistical methods.See, e.g., Kenney et al., Early Detection of Heart Failure UsingElectronic Health Records, Circ. Cardiovasc. Qual. Outcomes, 9:649-658,2016; Deo & Nallamothu, Learning About Machine Learning: The Promise andPitfalls of Big Data and the Electronic Health Record, Circ. Cardiovasc.Qual. Outcomes, 9:618-620, 2016; Passantino et al., Predicting mortalityin patients with acute heart failure: Role of risk scores, World J.Cardiol., 7(12): 902911, 2015; Thorvaldsen et al., Predicting Risk inPatients Hospitalized for Acute Decompensated Heart Failure andPreserved Ejection Fraction, Circ. Heart Fail., 10:e003992, 2017.

However, none of the studies cited above describe systems or methods forpredicting an intra-aortic pressure of a patient receiving hemodynamicsupport. Some of the cited studies describe systems or methods forpredicting a peripheral blood pressure of a patient. However, peripheralblood pressure provides an indirect indication of a patient's cardiacfunction, whereas an intra-aortic pressure provides a direct indicationof a patient's cardiac function. Peripheral blood pressure may beobtained using, for example, a blood pressure cuff wrapped around anextremity of a patient (e.g., an arm cuff or a wrist cuff), whereas anintra-aortic pressure may be obtained using, for example, atransvalvular micro-axial heart pump. As a result, a peripheral bloodpressure is less informative of a patient's condition than anintra-aortic pressure.

Additionally, some of these approaches described in the studies citedabove are not practical, at least from a clinical point of view, becausethey require an extensive number of input variables. Moreover, some ofthe variables used in the studies cited above are not easily measurable.Furthermore, some of the models proposed in the studies cited above areonly suitable for evaluating long-term mortality. They cannot helpphysicians with real-time early detection of diseases, such as ADHF.

Patients with severe multi-vessel coronary artery disease (CAD),unprotected left main coronary artery stenosis, last remaining patentvessel, and/or severely reduced left ventricular (LV) ejection fraction(EF) are often turned down from cardiac surgery and are increasinglyreferred for high-risk percutaneous coronary intervention (HR-PCI).Transvalvular micro-axial heart pumps, such as the Impella 5.0® fromAbiomed, Inc., Danvers, Mass., shown in FIG. 1(a), are increasingly usedduring HR-PCI to prevent hemodynamic instability and improve clinicaloutcomes. See, e.g., Russo et al., Hemodynamics and its predictorsduring transvalvular-micro-axial-heart-pump-protected PCI in high riskpatients with reduced ejection fraction, Int. J. Cardiol. 274:221-225,2019; Dixon et al., A prospective feasibility trial investigating theuse of the transvalvular micro-axial heart pump system in patientsundergoing high-risk percutaneous coronary intervention (TheTransvalvular Micro-axial Heart Pump Trial): initial U.S. experience,JACC Cardiovasc. Interv. 2 (2) 91-96, 2009; O'Neill et al., Aprospective, randomized clinical trial of hemodynamic support withtransvalvular micro-axial heart pump versus intra-aortic balloon pump inpatients undergoing high-risk percutaneous coronary intervention: thetransvalvular micro-axial heart pump study, Circulation 126 (14)1717-1727, 2012.

A transvalvular micro-axial heart pump is a percutaneous, catheter-baseddevice that provides hemodynamic support to the heart of a patient. Asshown in FIG. 1(a), a transvalvular micro-axial heart pump 110 mayinclude a pigtail 111, an inlet area 112, a cannula 113, a pressuresensor 114, an outlet area 115, a motor housing 116, and/or a cathetertube 117. Pigtail 111 may assist with stabilizing transvalvularmicro-axial heart pump 110 in the heart of a patient. During operation,blood may be drawn into one or more openings of inlet area 112,channeled through cannula 113, and expelled through one or more openingsof outlet area 115 by a motor (not shown) disposed in motor housing 116.In some implementations, pressure sensor 114 may include a flexiblemembrane that is integrated into cannula 113. One side of pressuresensor 114 may be exposed to the blood pressure on the outside ofcannula 113, and the other side may be exposed to the pressure of theblood inside of cannula 113. In some such implementations, pressuresensor 114 may generate an electrical signal proportional to thedifference between the pressure outside cannula 113 and the pressureinside cannula 113. In some implementations, a pressure differencemeasured by pressure sensor 114 may be used to position transvalvularmicro-axial heart pump 110 within the heart of a patient. In someimplementations, pressure sensor 114 is an optical pressure sensor.Catheter tube 117 may provide one or more fluidic and/or electricalconnections between transvalvular micro-axial heart pump 110 and more ormore other devices of a ventricular support system.

As shown in FIG. 1(b), transvalvular micro-axial heart pump 110 may bepositioned in a patient's heart 120. As shown, transvalvular micro-axialheart pump 110 may, for example, be inserted percutaneously via thefemoral artery 122 into the ascending aorta 124, across the aortic valve126, and into the left ventricle 128. In other implementations, atransvalvular micro-axial heart pump may, for example, be insertedpercutaneously via the axillary artery 123 into the ascending aorta 124,across the aortic valve 126, and into the left ventricle 128. In otherimplementations, a transvalvular micro-axial heart pump may, forexample, be inserted directly into the ascending aorta 124, across theaortic valve 126, and into the left ventricle 128. During operation,transvalvular micro-axial heart pump 110 entrains blood from the leftventricle 128 and expels blood into the ascending aorta 124. As aresult, transvalvular micro-axial heart pump 110 performs some of thework normally done by the patient's heart 120. The hemodynamic effectsof transvalvular micro-axial heart pumps include an increase in cardiacoutput, improvement in coronary blood flow resulting in a decrease in LVend-diastolic pressure, pulmonary capillary wedge pressure, myocardialworkload, and oxygen consumption. See, e.g., Burkhoff & Naidu, Thescience behind percutaneous hemodynamic support: a review and comparisonof support strategies, Catheter Cardiovasc. Interv. 80:816-29, 2012.

As shown in FIG. 1(c), transvalvular micro-axial heart pump 110 may beincorporated into a ventricular support system 100. Ventricular supportsystem 100 also includes a controller 130 (e.g., an Automated ImpellaController® from Abiomed, Inc., Danvers, Mass.), a display 140, a purgesubsystem 150, a connector cable 160, a plug 170, and a repositioningunit 180. As shown, controller 130 includes display 140. Controller 130monitors and controls transvalvular micro-axial heart pump 110. Duringoperation, purge subsystem 150 delivers a purge fluid to transvalvularmicro-axial heart pump 110 through catheter tube 117 to prevent bloodfrom entering the motor (not shown) within motor housing 116. In someimplementations, the purge fluid is a dextrose solution (e.g., 5%dextrose in water with 25 or 50 IU/mL of heparin). Connector cable 160provides an electrical connection between transvalvular micro-axialheart pump 110 and controller 130. Plug 170 connects catheter tube 117,purge subsystem 150, and connector cable 160. In some implementations,plug 170 includes a memory for storing operating parameters in case thepatient needs to be transferred to another controller. Repositioningunit 180 may be used to reposition transvalvular micro-axial heart pump110.

As shown, purge subsystem 150 includes a container 151, a supply line152, a purge cassette 153, a purge disc 154, purge tubing 155, a checkvalve 156, a pressure reservoir 157, an infusion filter 158, and asidearm 159. Container 151 may, for example, be a bag or a bottle. Apurge fluid is stored in container 151. Supply line 152 provides afluidic connection between container 151 and purge cassette 153. Purgecassette 153 may control how the purge fluid in container 151 isdelivered to transvalvular micro-axial heart pump 110. For example,purge cassette 153 may include one or more valves for controlling apressure and/or flow rate of the purge fluid. Purge disc 154 includesone or more pressure and/or flow sensors for measuring a pressure and/orflow rate of the purge fluid. As shown, controller 130 includes purgecassette 153 and purge disc 154. Purge tubing 155 provides a fluidicconnection between purge disc 154 and check valve 156. Pressurereservoir 157 provides additional filling volume during a purge fluidchange. In some implementations, pressure reservoir 157 includes aflexible rubber diaphragm that provides the additional filling volume bymeans of an expansion chamber. Infusion filter 158 helps preventbacterial contamination and air from entering catheter tube 117. Sidearm159 provides a fluidic connection between infusion filter 158 and plug170.

During operation, controller 130 receives measurements from pressuresensor 114 and purge disc 154 and controls the motor (not shown) withinmotor housing 116 and purge cassette 153. As noted above, controller 130controls and measures a pressure and/or flow rate of a purge fluid viapurge cassette 153 and purge disc 154. During operation, after exitingpurge subsystem 150 through sidearm 159, the purge fluid is channeledthrough purge lumens (not shown) within catheter tube 117 and plug 170.Sensor cables (not shown) within catheter tube 117, connector cable 160,and plug 170 provide an electrical connection between pressure sensor114 and controller 130. Motor cables (not shown) within catheter tube117, connector cable 160, and plug 170 provide an electrical connectionbetween the motor within motor housing 116 and controller 130. Duringoperation, controller 130 receives measurements from pressure sensor 114through the sensor cables and controls the electrical power delivered tothe motor within motor housing 116 through the motor cables. Bycontrolling the power delivered to the motor within motor housing 116,controller 130 can control the speed of the motor within motor housing116.

Various modifications can be made to ventricular support system 100 andone or more of its components. For example, as detailed in Abiomed,Impella® Ventricular Support Systems for Use During Cardiogenic Shockand High-Risk PCI: Instructions for Use and Clinical Reference Manual,Document No. 0042-9028 rG (April 2020), which is incorporated herein byreference, ventricular support system 100 can be modified to accommodateother types of transvalvular micro-axial heart pumps, such as theImpella 2.5®, Impella LD®, and Impella CP® catheters. As anotherexample, one or more sensors may be added to transvalvular micro-axialheart pump 100. For example, as described in U.S. patent applicationSer. No. 16/353,132, which was filed on Mar. 14, 2019 and is entitled“Blood Flow Rate Measurement System,” and which is incorporated hereinby reference, a signal generator may be added to transvalvularmicro-axial heart pump 100 to generate a signal indicative of therotational speed of the motor within motor housing 116. As anotherexample, a second pressure sensor may be added to transvalvularmicro-axial heart pump 100 near inlet area 112 that is configured tomeasure a left ventricular blood pressure. In such implementations,additional sensor cables may be disposed within catheter tube 117,connector cable 160, and plug 170 to provide an electrical connectionbetween the one or more additional sensors and controller 130. As yetanother example, one or more components of ventricular support system100 may be separated. For example, display 140 may be incorporated intoanother device in communication with controller 130 (e.g., wirelessly orthrough one or more electrical cables).

FIGS. 2(a)-(h) illustrate different screens that may be displayed bydisplay 140. For example, FIG. 2(a) illustrates a home screen 202 thatincludes a heart pump type 211 (e.g., “Impella 5.0”), a heart pumpserial number 212 (e.g., “171000”), a date and time 214 (e.g.,“2019-08-21 15:56”), a software version number 216 (e.g., “IC4048V8.1”), a power source icon 218 (e.g., a battery indicator), buttonlabels 221, 222, 224, 226, and 228 (e.g., “mute alarm,” “flow control,”“display,” “purge menu,” and “menu”), a present heart pump speed(performance) setting 230 (e.g., “P-4”), heart pump flow measurements242, purge system measurements 244, a status indicator 251 (e.g.,“Impella Position OK”), a diagram 261, and a notification area 270.Present heart pump speed (performance) setting 230 corresponds with aspeed at which the motor within motor housing 116 is operating. Forexample, “P-4” may indicate that the motor within motor housing 116 isoperating at approximately 22,000 rpm. Heart pump flow measurements 242include a mean flow (e.g., “1.6 L/min”), a minimum flow (e.g., “1.1L/min”), and a maximum flow (e.g., “2.1 L/min”) of blood throughtransvalvular micro-axial heart pump 100. Heart pump flow measurements242 may be derived from measurements obtained by pressure sensor 114and/or an energy intake of the motor within motor housing 116. Purgesystem measurements 244 include a current flow (e.g., “10.2 ml/hr”) anda current pressure (e.g., “99 mmHg”) of purge fluid through purgesubsystem 150. Purge system measurements 244 may be derived frommeasurements obtained by purge disc 154. Diagram 161 illustrates howtransvalvular micro-axial heart pump 110 should be positioned in apatient's heart. In FIG. 2(a), notification area 270 includesnotifications 271, 272, and 273.

Each of notifications 271, 272, and 273 includes a header and a set ofinstructions. For example, notification 271 includes the header “PurgeSystem Open” and instructions to “1. Check the purge system tubing foropen connections or leaks” and “2. Press the Purge Menu soft key thenselect Change Cassette & Bag.” Notification 272 includes the header“Suction” and instructions to “1. Reduce P-Level,” “2. Check filling andvolume status,” and “3. Check Impella position.” Notification 273include the header “Flight Mode Enabled” and instructions to “1. Connectcontroller to ground during air transport,” “2. If equipped with ImpellaConnect, enable Flight Mode on module,” and “3. Upon arrival atreceiving hospital, disable Flight Mode under MENU.” In otherimplementations, notifications displayed in notification area 270 may bestructure differently. For example, the header and instructions may becontained in a single box, as opposed to two different boxes. As anotherexample, the notifications may not include a header. As yet anotherexample, the instructions may be replaced with a different type ofinformation, such as an explanatory statement. For example, anotification may serve as an alert and include a statement describingthe cause of the alert.

FIGS. 2(b)-(d) illustrate a placement screen 204, a purge screen 206,and an infusion history screen 208, respectively. A user may switchbetween these screens using buttons positioned alongside button labels221, 222, 224, 226, and 228. In other implementations, different userinput devices may be used. For example, in some implementations, display140 may be a touchscreen and a user may switch between screens bytapping button labels 221, 222, 224, 226, and 228. As another example,in some implementations, a user may use a separate input device, such asa mouse or a keyboard, to switch between screens.

With the exception of status indicator 251, diagram 261, andnotifications 271, 272, and 273, all of the data fields from home screen202 are included in placement screen 204, purge screen 206, and infusionhistory screen 208. In other implementations, additional data fields maybe added or removed from these screens. For example, in someimplementations, heart pump type 211 and heart pump serial number 212may only appear on main screen 202.

Placement screen 204, purge screen 206, and infusion history screen 208also include additional information. For example, as shown in FIG. 2(b),placement screen 204 includes a placement signal graph 252, placementsignal measurements 262, a motor current graph 253, and motor currentmeasurements 263. Placement signal graph 252 illustrates pressure valuesderived from measurements obtained by pressure sensor 114 over a periodof time (e.g., “10 sec.”). Placement signal measurements 262 include amean pressure value (e.g., “9 mmHg”), a minimum pressure value (e.g.,“−17 mmHg”), and a maximum pressure value (e.g., “76 mmHg”) derived frommeasurements obtained by pressure sensor 114 over the period of time.Motor current graph 253 illustrates current values provided to the motorwithin motor housing 116 over a period of time (e.g., “10 sec.”). Motorcurrent measurements 263 include a mean current (e.g., “535 mA”), aminimum current (e.g., “525 mA”), and a maximum current (e.g., “556 mA”)provided to the motor within motor housing 116 over the period of time.Collectively, placement signal graph 252, placement signal measurements262, motor current graph 253, and motor current measurements 263 areuseful for determining a position of transvalvular micro-axial heartpump 110 within the heart of a patient.

As shown in FIG. 2(c), purge screen 206 additionally includes a purgeflow graph 254, purge flow measurements 264, a purge pressure graph 255,and purge pressure measurements 265. Purge flow graph 254 illustrates aflow rate of a purge fluid through purge subsystem 150 over a period oftime (e.g., “1 hr.”). Purge flow measurements 264 include a current flowrate of a purge fluid through purge subsystem 150 (e.g., “17.9 ml/hr”).Purge pressure graph 255 illustrates a pressure of a purge fluid inpurge subsystem 150 over a period of time (e.g., “1 hr.”). Purgepressure measurements 265 include a current pressure of a purge fluid inpurge subsystem 150 (e.g., “559 mmHg”). Collectively, purge flow graph254, purge flow measurements 264, purge pressure graph 255, and purgepressure measurements 265 can assist with patient management.

As shown in FIG. 2(d), infusion history screen 208 additionally includesan infusion history table 256, dextrose infusion measurements 266, andheparin infusion measurements 267. Infusion history table 256 provides asummary of the amount of purge fluid, heparin, and dextrose delivered tothe patient over each of a plurality of time periods (e.g.,“10:00-11:00,” “11:00-12:00,” “12:00-13:00,” “13:00-14:00,”“14:00-15:00,” and “15:00-15:08”). Dextrose infusion measurements 266include a current rate at which dextrose is being delivered to thepatient (e.g., “935 mg/hr”). Heparin infusion measurements 267 include acurrent rate at which heparin is being delivered to the patient (e.g.,“935 IU/hr”). Collectively, infusion history table 256, dextroseinfusion measurements 266, and heparin infusion measurements 267 canalso assist with patient management.

FIGS. 2(e)-(h) illustrate how different types of alerts may be presentedto a user through display 140. For example, when a patient has poornative ventricular function and controller 130 cannot determine aposition of transvalvular micro-axial heart pump 110 within the heart ofthe patient, home screen 202 may be updated in the manner shown FIG.2(e). More specifically, status indicator 251 may be updated to state“Impella Position Unknown” and notification 274 may be added tonotification area 270. As another example, when transvalvularmicro-axial heart pump 110 is fully in the ventricle or the aorta of thepatient, placement screen 204 may be updated in the manner shown FIG.2(f). More specifically, notification 275 may be added to notificationarea 270. As yet another example, when outlet area 115 is positioned onor near the aortic valve of the patient, placement screen 204 may beupdated in the manner shown FIG. 2(g). More specifically, notification276 may be added to notification area 270. As yet another example, whenpressure sensor 114 fails and controller 130 is unable to calculateheart pump flow measurements 242, placement screen 204 may be updated inthe manner shown FIG. 2(h). More specifically, heart pump flowmeasurements 242 may be replaced with a table of estimated flows andcorresponding MAPs and notification 277 may be added to notificationarea 270.

FIG. 3 illustrates a system 300 for monitoring and/or controlling aplurality of medical device controllers, such as controller 130. System300 may include medical device controllers 312, 314, 316, and 318,computer network 322, local area network (LAN) 324, remote link module332, router 334, wireless access point 336, cell site 338, server 342,data store 344, OCR engine 346, and/or monitoring stations 352 and 354.Computer network 322 may include wired and/or wireless segments and/ornetworks. For example, computer network 322 may include wirelessnetworks that conform to an IEEE 802.11x standard (e.g., wireless localarea networks (WLANs), commonly referred to as “Wi-Fi”), represented bywireless access point 336, and/or cellular networks, represented by cellsite 338. As another example, computer network 322 may include privateand/or public networks, such as LAN 324, metropolitan area networks(MANs), and/or wide area networks (WANs), such as the Internet (notshown).

System 300 illustrates a few different ways in which medical devicecontrollers can be connected to computer network 322. For example,medical device controller 312 is directly connected to computer network322. As another example, medical device controller 314 is optionallyconnected to computer network 322 through remote link module 332. As yetanother example, medical device controller 316 is connected to computernetwork 322 through LAN 324 and router 334. As yet another example,medical device controller 318 is connected to computer network 322through LAN 324, router 334, and wireless access point 336. Medicaldevice controller 318 is also connected to computer network 322 throughcell site 338. In other implementations, medical device controllers maybe added and/or removed from system 300. Furthermore, multiple medicaldevice controllers may be connected to computer network 322 in a similarmanner. For example, a plurality of medical device controllers may bedirectly connected to computer network 322, much like medical devicecontroller 312.

Server 342 may be configured to request status information from medicaldevice controllers 312, 314, 316, and 318 through computer network 322.In some implementations, server 342 requests the status informationautomatically and/or repeatedly. In some implementations, the statusinformation includes an image of the contents of a screen displayed by adisplay associated with medical device controllers 312, 314, 316, and/or318. For example, the status information may be similar to an image ofany one of the screens illustrated in FIGS. 2(a)-(h). The image may besent in one or more messages encoded as a video frame or a sequence ofvideo frames. Furthermore, the video frame(s) may, for example, containpixelated copies of the image. In some implementations, the statusinformation includes information from one or more of the data fieldsdisplayed by a display associated with medical device controllers 312,314, 316, and/or 318. For example, the status information may includeinformation from one or more of the data fields similar to heart pumptype 211, heart pump serial number 212, date and time 214, present heartpump speed (performance) setting 230, heart pump flow measurements 242,purge system measurements 244, status indicator 251, and/or notificationarea 270.

Server 342 may also be configured to process the received statusinformation. For example, when server 342 receives an image of thecontents of a screen displayed by a display associated with medicaldevice controllers 312, 314, 316, and/or 318, server 342 may parse theimages and extract textual information by optical character recognizing(OCR) portions of the image. In some implementations, the extractedtextual information includes information from one or more of the datafields displayed by a display associated with medical device controllers312, 314, 316, and/or 318. In some implementations, server 342 includesan OCR engine for parsing images and extracting textual information. Insome implementations, server 342 communicates with an external OCRengine, such as OCR engine 346, for parsing images and extractingtextual information.

Data store 344 may be configured to store unprocessed and/or processedstatus information. For example, data store 344 may store an image ofthe contents of a screen displayed by a display associated with medicaldevice controllers 312, 314, 316, and/or 318 and/or textual informationextracted from the image by server 342 and/or OCR engine 346. Data store344 may also be configured to provide at least some of the unprocessedand/or processed status information to monitoring stations 352 and 354upon request. Monitoring stations 352 and 354 may be, for example, aphone, tablet, and/or computer. In some implementations, monitoringstations 352 and 354 may use cloud-based technology to securely andremotely display at least some of the unprocessed and/or processedstatus information on associated displays. For example, monitoringstations 352 and 354 may use an online device management system, such asthe Impella Connect® from Abiomed, Inc., Danvers, Mass., to securely andremotely display at least some of the unprocessed and/or processedstatus information.

In some implementations, server 342 and/or monitoring stations 352and/or 354 may also be configured to remotely send commands to one ormore medical device controllers within system 300 (e.g., medical devicecontrollers 312, 314, 316, and/or 318). For example, if controller 130is added to system 300, server 342 and/or monitoring stations 352 and/or354 may be configured remotely adjust the power delivered to the motorwithin motor housing 116, the flow rate of a purge fluid through purgesubsystem 150, and/or the pressure of a purge fluid in purge subsystem150 by remotely sending a command to controller 130. In someimplementations, one or more medical device controllers within system300 (e.g., medical device controllers 312, 314, 316, and/or 318) mayoffload one or more computations to server 342 and/or monitoringstations 352 and/or 354. For example, if controller 130 is added tosystem 300, controller 130 may offload complex calculations (e.g.,machine learning algorithms) to server 342 and/or monitoring stations352 and/or 354. To reduce latency, controller 130 may also offload suchcalculations to another computing device on the same LAN (not shown).

As shown in FIG. 4 , the cardiac cycle contains four phases: isovolumiccontraction phase 410, ejection phase 420, isovolumic relaxation phase430, and filling phase 440. During the cardiac cycle, the contractionand relaxation of the heart muscles in the heart chamber causes twovalves, mitral valve 452 and aortic valve 454, to open and close due topressure differences. During isovolumic contraction phase 410, mitralvalve 452 and aortic valve 454 are closed and the pressure in chamber456 increases until it is so high that aortic valve 454 opens. Duringejection phase 420, mitral valve 452 is closed, aortic valve 454 isopen, and blood flows out of chamber 456 into the aorta. Duringisovolumic relaxation phase 430, mitral valve 452 and aortic valve 454are closed and pressure in chamber 456 decreases until it is so low thatmitral valve 452 opens. During filling phase 440, mitral valve 452 isopen, aortic valve 454 is closed, and blood flows into chamber 456. Thefirst two phases are known as systole and the last two phases are knownas diastole.

FIG. 5 illustrates the regular waveforms of Intra-Aortic Pressure (AoP),Left Ventricular Pressure (LVP), Differential Pressure (dP), Pump Flow,and Motor Current, as well as their relationships with systole anddiastole. The AoP waveform corresponds with the pressure in theascending aorta of a patient (e.g., ascending aorta 124). The LVPwaveform corresponds with the pressure in the left ventricle of thepatient (e.g., left ventricle 128). The dP waveform corresponds with thepressure differential between the ascending aorta and left ventricle ofthe patient. The Pump Flow waveform corresponds with a rate at whichblood is drawn into the ascending aorta from the left ventricle by atransvalvular micro-axial heart pump (e.g., transvalvular micro-axialheart pump 110). The Motor Current waveform corresponds with the currentprovided to a motor of the transvalvular micro-axial heart pump (e.g.,the motor within motor housing 116).

Maintenance of a constant mean intra-aortic pressure (MAP) is vital toensure adequate organ perfusion. See, e.g., Chemla et al., Mean aorticpressure is the geometric mean of systolic and diastolic aortic pressurein resting humans, Journal of Applied Physiology 99:6, 2278-2284, 2005.Studies show that increases in the duration of time spent below a MAPthreshold of 65 mmHg are associated with worse patient outcomes, such asrisk of mortality or organ dysfunction. See, e.g., Varpula et al.,Hemodynamic variables related to outcome in septic shock, Intensive CareMed. 31:1066-1071, 2005; Dunser et al., Arterial blood pressure duringearly sepsis and outcome, Intensive Care Med. 35:1225-1233, 2009; Dunseret al., Association of arterial blood pressure and vasopressor load withseptic shock mortality: a post hoc analysis of a multicenter trial,Crit. Care Lond. Engl. 13:R181, 2009. As shown in FIG. 5 , physiologicwaveforms obtained using catheter-based hemodynamic support devices,such as a transvalvular micro-axial heart pump, can be a rich source ofhemodynamic information. However, forewarnings regarding a patient'sstatus based on a forecasted time series of MAP using such devices isscarce.

Aspects of the present disclosure describe systems and methods forpredicting an intra-aortic pressure of a patient receiving hemodynamicsupport from a transvalvular micro-axial heart pump. Advance warning ofimminent changes in intra-aortic pressure (e.g., MAP), even if thewarning comes only 5 to 15 minutes ahead, can aid in prompt managementof a patient prior to a total hemodynamic collapse. For example, if apatient's intra-aortic pressure is predicted to increase or remainstable, then a clinician may initiate or continue a percutaneouscoronary intervention (PCI) procedure. Similarly, if a patient'sintra-aortic pressure is predicted to decrease, then a clinician maydelay or terminate a PCI procedure. Generally, significant decreases ina patient's predicted intra-aortic pressure (e.g., decreases of at least10 mmHg) indicate that the patient's condition is worsening. However, asustained increase may also indicate that the patient's condition isdeteriorating.

Forecasting stable trends in the intra-aortic pressure can also serve asa signal to wean the patient off the transvalvular micro-axial heartpump. Similarly, a projected intra-aortic pressure could be used toassign the level of support provided to the patient during the weaningprocess. For example, a clinician may adjust the pharmacological supportprovided to the patient based on a predicted intra-aortic pressure(e.g., by adjusting an amount of a medication, such as a vasopressor oran inotrope, provided to the patient). As another example, a motor speedsetting (e.g., present heart pump speed (performance) setting 230) canbe manually adjusted by a clinician and/or automatically adjusted by aconnected medical device controller (e.g., controller 130) based on theprojected intra-aortic pressure. For example, in some implementations,the medical device controller may be configured to wean a patient offsupport by automatically and gradually decreasing the motor speedsetting over time. In such implementations, the medical device may, forexample, temporarily increase the motor speed setting if the patient'scondition is predicted to worsen (e.g., the patient's intra-aorticpressure is predicted to significantly decrease).

In some implementations, a display associated with a transvalvularmicro-axial heart pump (e.g., display 140) may be configured to displaya predicted intra-aortic pressure so that a clinician can reactaccordingly. For example, in relation to the screens illustrated inFIGS. 2(a)-(h), the predicted intra-aortic pressure may be displayedalongside heart pump flow measurements 242 and/or purge systemmeasurements 244. As another example, any significant changes inintra-aortic pressure (e.g., +/−10 mmHg) may cause a notification to bedisplayed in notification area 270 or an update to status indicator 251.As yet another example, an intra-aortic pressure forecasting screen maybe displayed that includes a graph of the predicted intra-aorticpressure over time, much like placement signal graph 252. As yet anotherexample, a graph of the predicted intra-aortic pressure over time may beadded to home screen 202, placement screen 204, purge screen 206, and/orinfusion history screen 208 and/or replace a data field in one of thosescreens (e.g., placement signal graph 252, motor current graph 253,purge flow graph 254, and/or purge pressure graph 255).

As explained above, a transvalvular micro-axial heart pump not onlyprovides hemodynamic support, thus aiding in native heart functionrecovery, but it is also equipped with, for example, one or more sensors(e.g., pressure sensor 114) to capture measurements at origin, insteadof peripherally. Collectively, the measurements obtained from the one ormore sensors of a transvalvular micro-axial heart pump and the operatingcharacteristics of the motor of the transvalvular micro-axial heart pump(e.g., the motor within motor housing 116) can provide a rich set ofdata to which a machine learning algorithm can be applied to predict anintra-aortic pressure of a patient. For example, a machine learningalgorithm can be applied to a set of features including intra-aorticpressure, motor current, motor speed, and/or a motor speed setting(e.g., P-0, P-1, P-2, P-3, P-4, P-5, etc. for an Impella Catheter fromAbiomed, Inc., Danvers, Mass.). Intra-aortic pressure may be derivedfrom measurements obtained by the pressure sensor of the transvalvularmicro-axial heart pump. Motor current may be derived from an energyintake of the motor of the transvalvular micro-axial heart pump. Motorspeed may be derived from measurements obtained by a signal generator ofthe transvalvular micro-axial heart pump. Motor speed may also bederived from a back electromotive force (EMF) of the motor of thetransvalvular micro-axial heart pump. In some implementations, the motorof the transvalvular micro-axial heart pump includes three or more motorwindings. In such implementations, the back EMF may be derived from, forexample, a measured voltage across a motor winding disconnected from apower supply. In some implementations, the power supply may be in aconnected medical device controller (e.g., controller 130).

A variety of different machine learning algorithms, such as Bayesianalgorithms, clustering algorithms, decision tree algorithms,dimensionality reduction algorithms, instance-based algorithms, deeplearning algorithms, regression algorithms, regularization algorithms,and rule-based machine learning algorithms, can be applied tomeasurements from a transvalvular micro-axial heart pump to predict anintra-aortic pressure of a patient. Some examples of deep learningalgorithms include the Autoregressive Integrated Moving Average (ARIMA)models, Deep Neural Network (DNN) models, Recurrent Sequence to Sequencemodels, Recurrent Sequence to Sequence models with Attention,Transformer models, Temporal Convolutional Neural Network (TCN) models,and Convolutional Neural Pyramid models. In some implementations, thesemachine learning algorithms may be implemented by a medical devicecontroller connected to the transvalvular micro-axial heart pump (e.g.,controller 130). In other implementations, some or all of thisprocessing may be offloaded to another device over a computer network(e.g., server 342).

The ARIMA model is a popular statistical method for time seriesforecasting. The components of the model are Autoregression (AR),Integrated, and Moving Average (MA). As a result, this model uses (a)the dependent relationship between an observation and some number oflagged observations, (b) the differencing of raw observations(subtracting an observation from an observation at the previous timestep) in order to make the time series stationary, and (c) thedependency between an observation and a residual error from a movingaverage model applied to lagged observations. Additional informationregarding the ARIMA model can be found in Hyndman & Athanasopoulos,Forecasting: principles and practice, 2nd edition, Chapter 8 ARIMAmodels, OTexts: Melbourne, Australia, OTexts.com/fpp2, 2018, which isincorporated herein by reference.

A feed-forward Deep Neural Network (DNN) may be formed by one inputlayer, multiple hidden layers, and one output layer. A DNN may be usedin an autoregressive manner. In such implementations, a DNN may be builtwith a single unit in the output layer to perform one step aheadforecasting, and keep recursively feeding back the predictions formultiple steps ahead forecasting. Additional information regarding DNNmodels can be found in Schmidhuber, Deep Learning in Neural Networks: AnOverview, arXiv:1404.7828v4, 2014, which is incorporated herein byreference.

Recurrent Sequence to Sequence models map an input sequence to afixed-sized vector using one encoder, and then map the vector to atarget sequence with a decoder. Additional information regardingRecurrent Sequence to Sequence models can be found in Sutskever et al.,Sequence to Sequence Learning with Neural Networks, NeurIPS 2014, whichis incorporated herein by reference. Recurrent neural network (RNN)models may be used to retain the temporal information in the timeseries, as its hidden layers can memorize information processed throughshared weights. For the encoder, a bidirectional RNN model may be usedso that the model can process the data in both the forward and backwarddirections. In some implementations, two separate hidden layers may beused and then merged to the same output layer. For the decoder, an RNNmodel may be used to decode the target sequence from the hidden states.However, RNN models have trouble learning long-term dependencies due tovanishing gradients. Long Short-Term Memory (LSTM) Units can alleviatethe vanishing gradients issue with a memory cell state. The overallstructure 600 of a Recurrent Sequence to Sequence Model with LSTM unitsis illustrated in FIG. 6 . Additional information regarding LSTMs can befound in Hochreiter & Schmidhuber, Long Short-Term Memory, NeuralComputation, Volume 9 Issue 8, 1997, which is incorporated herein byreference. As used in the remainder of the present disclosure, aRecurrent Sequence to Sequence Models with LSTM units is simply referredto as an “LSTM.”

Recurrent Sequence to Sequence models need to compress all necessaryinformation of input into one fixed length vector from which to decodeeach output time step. As a result, it may be difficult for anencoder-decoder network to learn all useful information. Attentionmechanisms may be applied to alleviate this problem. Attentionmechanisms can learn local information by utilizing intermediate encoderstates for the context vectors used by the decoder. Thus, attentionmechanisms may be used, as opposed to functions, to overcome thedisadvantage of fixed-length context vector by creating shortcutsbetween the context vector and the entire source input. Additionalinformation regarding attention mechanisms can be found in Luong et al.,Effective Approaches to Attention-based Neural Machine Translation,arXiv:1508.04025, 2015, which is incorporated herein by reference.

The Legendre Memory Unit (LMU) further addresses the issue of vanishingand exploding gradients commonly associated with training RNNs by usingcell structure derived from first principles to project continuous-timesignals onto d orthogonal dimensions. The LMU provides theoreticalguarantees for learning long-range dependencies even as the discretetime-step, Δt, approaches zero. This enables the gradient to flow acrossthe continuous history of internal feature representations. The LMU is arecent innovation that achieves state-of-the-art memory capacity whileensuring energy efficiency, making it especially suitable for thechaotic time-series prediction task in the medical domain. Additionalinformation regarding the LMU can be found in Voelker et al., LegendreMemory Units: Continuous-Time Representation in Recurrent NeuralNetworks, NeurIPS 2019, which is incorporated herein by reference.

The Transformer model is a transduction model that relies entirely onself-attention (note that attention here is different from the onepreviously described) to compute representations of its input and outputwithout using sequence-aligned RNN or convolutions. Both the encodingand the decoding components are stacks of identical layers, each ofwhich is composed of two sublayers: one multi-head attention layer andone fully connected layer. The decoder has both those layers, butbetween them is an attention layer that helps the decoder focus on theoutput of the encoder stack. Instead of using a single scaleddot-product attention, the Transformer model projects the queries Q,keys K, and values V to an output as follows:

${{Attention}{}\left( {Q,K,V} \right)} = {{softmax}\left( {\frac{QK^{T}}{\sqrt{d_{k}}}V} \right)}$

The attention function is performed in parallel. In someimplementations, residual connections and dropout may be used in theTransformer model to improve performance. In the context of the presentdisclosure, since the Transformer model is being applied to a numerictime series, the absolute position in the input may be used instead ofpositional embedding. The overall structure 700 of a Transformer modelis illustrated in the FIG. 7 . As shown, the encoder contains onemulti-head attention layer and one fully connected layer and the decodercontains one masked multi-head attention layer, one multi-head attentionlayer and one fully connected layer. Additional information regardingthe Transformer model can be found in Vaswani et al., Attention Is AllYou Need, arXiv:1706.03762v5, 2017, which is incorporated herein byreference.

The TCN model has a convolutional hidden layer operating over aone-dimensional sequence. Convolutional neural networks createhierarchical representations over the input sequence in which nearbyinput elements interact at lower layers while distant elements interactat higher layers. This provides a shorter path to capture long-rangedependencies compared to the chain structure modeled by recurrentnetworks. In some implementations, the overall structure of a TCN modelincludes several convolutional blocks followed by a flatten layer andseveral fully connected layers. In some implementations, to equip themodel with a sense of order, the absolute position of input elements maybe embedded. In some implementations, to avoid the “dead relu” problem,the leaky relu activation function may be applied to each layer of theTCN model. In some implementations, dropout may be used to avoid overfitting. In some implementations, residual connections can be used toimprove the performance of the TCN model. The overall structure 800 of aTCN model is illustrated in the FIG. 8 . As shown, the TCN modelincludes multiple convolutional layers followed by a flatten layer andmultiple fully connected layers with residual connections. Additionalinformation regarding the TCN model can be found in Bai et al., AnEmpirical Evaluation of Generic Convolutional and Recurrent Networks forSequence Modeling, arXiv:1803.01271v2, 2018, which is incorporatedherein by reference.

Advantageously, the TCN model has a low memory requirement for training.Table 1 displays the complexity per layer of LMU, LSTM, DNN, Pyramid,TCN, and Transformer models. In Table 1, n is input length, d is modelhidden size, and k is kernel size. In the case of a long sequence, suchas a 5-minute real-time (RT) input sequence (e.g., having 7500 samples),LSTM models can easily use up all available memory and suffer from thevanishing gradient problem. Furthermore, the Transformer is highlyinefficient when the input length is bigger than the model hidden size.In contrast, TCN models can efficiently encode high frequency data.

TABLE 1 Complexity Per Layer LMU LSTM DNN TCN/Pyramid Transformer O(nd)O(nd²) O(d²) O(knd²) O(n²d)

In a Convolutional Neural Pyramid model, a cascade of features islearned in two streams. The first stream across different pyramid levelsenlarges the receptive field. The second stream learns information ineach pyramid level and finally merges it to produce the final result. Asshown in FIG. 9 , a structure 900 of a Convolutional Neural Pyramidmodel includes levels from 1 to N, where N is the number of levels. Wedenote these levels as L_(i) where i∈{1, . . . , N}. Different-scalecontent is encoded in each level L_(i). The feature extraction andreconstruction operations are applied to each level respectively. Theinput to L_(i) is the feature extracted from L_((i−1)) afterdownsampling. At level L_(i), 2i convolution layers are used to featureextraction. Then the reconstruction operation fuses information from twoneighboring levels. For instance, for L_(i) and L_(i+1), the output ofL_(i+1) is upsampled and then fused with the output from L_(i). In someimplementations, the downsampling operation is implemented as amaxpooling layer and upsampling operation is implemented as adeconvolution layer. Additional information regarding the ConvolutionalNeural Pyramid model can be found in Shen et al., Convolutional NeuralPyramid for Image Processing, arXiv:1704.02071v1 [cs.CV], 2017, which isincorporated herein by reference.

To test the effectiveness of some of the deep learning algorithmsdescribed above at predicting an intra-aortic pressure, patient datafrom 67 transvalvular micro-axial heart pump cases was obtained.Fifty-seven of these cases were indicated for HR-PCI (41 elective, 16urgent). The remaining 10 were indicated for acute myocardial infarction(AMI) cardiogenic shock (CGS). Additionally, another batch of 17transvalvular micro-axial heart pump cases were used to compare theperformance with respect to the amount of data.

The data from these cases included 25 HZ intra-aortic pressure, 25 HZmotor current, 25 HZ motor speed, and other waveforms (e.g., motor speedsettings, left ventricular pressure, and heart rate) derived from thesethree signals. The data was captured by medical device controllers(e.g., controller 130) connected to the transvalvular micro-axial heartpumps (e.g., transvalvular micro-axial heart pump 110). As used herein,a 25 HZ time series is referred to as real-time (RT) data. Averaged time(AT) data was derived from the RT data by averaging every 250 RT datapoints. In other implementations, different quantities of RT data pointsmay be average together to obtain AT data. In some implementations, thequantity of RT data points may be selected based on the desiredtimescale of the prediction. FIG. 10 illustrates a 10-second sample of a25 HZ RT Intra-Aortic Pressure and Motor Speed time series. FIG. 11illustrates a 20-minute sample of a 0.1 HZ AT Intra-Aortic Pressure timeseries. As shown, the waveform of average intra-aortic pressure isnonstationary and capable of indicating long-term trends of intra-aorticpressure and a patient's physical conditions.

Since features such as, motor speed settings, left ventricular pressure,and heart rate, can be derived from motor speed and intra-aorticpressure, only motor speed and intra-aortic pressure were used to testthe effectiveness of some of the deep learning algorithms describedabove. Motor current was also not included as a feature because theaverage sequence contains less variation in motor current than motorspeed and intra-aortic pressure. However, in other implementations, anyof these data sets may be use along with or instead of motor speedand/or intra-aortic pressure.

A sliding window was used to generate sequences of 15,000 samples (10mins). Sequences where sensor artifacts were not reflective ofphysiological MAPs (i.e. less than 50 mmHg, greater than 200 mmHg) wereremoved. A change in intra-aortic pressure greater than 10 mmHg wasconsidered significant. These time series were categorized into threetypes: increasing sequences (I), decreasing sequences (D), andstationary sequences (S). The overall changes of both increasingsequences and decreasing sequences were greater than 10 mmHg, and theoverall changes of stationary sequences were less than 10 mmHg.Ultimately, 50,705 increasing RT sequences, 50,577 decreasing RTsequences, and 419,559 stationary RT sequences were collected. All ofthese sequences were also converted to 0.1 HZ AT sequences of length 60.

Ten deep learning algorithms (i.e., ARIMA with averaged time (AT) input,DNN with AT input, LMU with AT input, LSTMs with AT input, LSTMs withAttention with AT input, TCN with real-time (RT) input, TCN with ATinput, Transformer with AT input, Pyramid with AT input, and Pyramidwith RT input) were trained to predict mean intra-aortic pressure (MAP)five minutes in advance. In other implementations, the forecastingwindow may be increased or decreased. For example, in otherimplementations, the forecasting window may be increased to 10 or 15minutes. The ten deep learning algorithms were also trained usingRMS-prop optimizer and a learning rate decay of 0.8. A 60%-20%-20%training-validation-test split was used. Since there are many possiblecombinations of hyper-parameters, a hyper-parameter random grid searchwas performed on a 10% hold out dataset. See, e.g., Bergstra & Bengio,Random Search for Hyper-Parameter Optimization, Journal of MachineLearning Research 13 281-305, 2012. The hyper-parameter search rangescan be found in Table 2. A Root Mean Squared Error (RMSE) was used as anevaluation metric. A computed moving average of RMSE on the validationset was used as an early stopping criteria. The same batch size of 64was used for all tests.

TABLE 2 Hyper-Parameter Random Search Range ARIMA #Moving #Lags#Differenced Average 1~10 0~3 0~3 LMU Learning #Layers Hidden Rate 0~9Size 0.1~0.00001 64~512 NN Learning #Layers Hidden Rate 0~9 Size0.1~0.00001 64~512 LSTMs Learning Dropout #Encoder #Decoder Hidden(Attention) Rate Rate Layer Layer Size 0.1~0.00001 0~0.9 1~3 1~3 64~512TCN Learning Dropout #Encoder #Decoder Hidden Rate Rate Layer Layer Size0.1~0.00001 0~0.9 2~9 2~9 64~512 Transformer Learning Dropout Model FFSize #Layers #Heads Rate Rate Size 64~512 2~6 2~8 0.1~0.00001 0~0.964~512 Pyramid Learning Dropout Hidden #Decoder #Mapping #Levels RateRate Size Layer Layer 2~6 0.1~0.00001 0~0.9 64~512 1~5 1~3

FIG. 12 provides a comparison of the average RMSEs achieved by some ofthe tested deep learning algorithms. From left to right, each of the barplots provides the average RMSE achieved by LMU with AT input, LSTM withAT input, LSTM with Attention with AT input, DNN with AT input, TCN withAT input, Transformer with AT input, and Pyramid with AT input. Asshown, the models were tested on an increasing (I) only dataset, adecreasing (D) only dataset, a stationary (S) only dataset, and an I-D-Sdataset. The I-D-S dataset contained equal proportions of all threetypes of sequences. 50,000 sequences of samples were included in the I,D, and S datasets. 150,000 sequences of samples were included in theI-D-S datasets. All of the models were trained on an I-D-S dataset.Overall, the LMU model consistently achieved the best average RMSEscores, including an average RMSE of 1.837 mmHg on the I-D-S dataset.

FIGS. 13 and 14 illustrate the MAP forecasts generated by the twotop-performing models, LMU with AT input and LSTM with Attention with ATinput. FIG. 13 illustrates the MAP forecasts against the ground truth(e.g., the true intra-aortic pressure) for a single recording over thecourse of 24 hours. The black line is the ground truth and the coloredlines are the model predictions. FIG. 14 illustrates the MAP forecastson increasing sequences, decreasing sequences and stationary sequences.The dashed line is the ground truth and the solid lines are the modelpredictions. The prior five minutes of intra-aortic pressure and motorspeed are the inputs to generate the predicted intra-aortic pressurevalues. As shown, both models closely follow the ground truth.

Table 3 displays all RMSE values (mmHg) per cohort for the modelstrained on permutations of the Increasing-Decreasing-Stationary (I, D,S) data sets. The top number in each entry is the RMSE result of thecombined cohort. The three values in parenthesis are RMSEs on each ofthree test sets, which only contained increasing, decreasing, andstationary sequences, respectively. All results are averages of fiveruns. The I-D-S training set contained equal proportions of all threetypes of sequences. The I-D only training set contained equalproportions of increasing sequences and decreasing sequences. The I-Sonly training set contained equal proportions of increasing sequencesand stationary sequences. The D-S only training set contained equalproportions of decreasing sequences and stationary sequences.

FIG. 15 provides a comparison of the average RMSEs achieved by some ofthe tested deep learning algorithms. From left to right, each of the barplots provides the average RMSE achieved by LMU with AT input, LSTM withAT input, LSTM with Attention with AT input, DNN with AT input, TCN withAT input, Transformer with AT input, and Pyramid with AT input.Different training and test datasets were used with these models. Thelight gray portion of each bar represents the prediction performanceimprovement between the initial patient cohort (N=20) and the currentpatient cohort (N=67). Each model was trained on permutations of theIncreasing-Decreasing-Stationary (I, D, S) data sets, as described abovein relation to Table 3. Furthermore, each model was tested on anincreasing (I) only dataset, a decreasing (D) only dataset, a stationary(S) only dataset, and an I-D-S dataset, as described above in relationto FIG. 12 . Without stationary sequences in the training set, allmodels can achieve comparable or even better performance for predictingstationary sequences. Furthermore, the improvement illustrated aboveeach bar demonstrates a potential for even better model performance asmore data is collected in the future.

TABLE 3 Models\Training Sets I-D-S I-D I-S D-S ARIMA(AT) 15.943(10.151-10.089-7.894) 13.999 (10.713-9.11-6.444) 19.16(8.556-9.068-4.549) 16.73 (10.176-8.703-8.058) NN(AT)  4.842(6.337-5.434-3.488)  5.809 (5.968-5.73-4.111) 4.519 (6.116-5.393-2.094) 4.39 (5.525-5.756-2.118) LMU(AT)  1.837 (2.507-2.491-0.545)  2.143(2.111-2.19-0.825) 2.079 (2.621-3.088-0.572) 2.011 (2.901-2.64-0.491)LSTM(AT)  3.363 (4.577-4.468-2.211)  4.603 (4.92-4.619-3.508) 3.359(4.609-6.131-2.064) 3.638 (6.17-4.789-2.041) LSTM_Attention(AT)  3.799(4.904-4.686-2.102)  4.746 (5.031-4.841-3.158) 3.118 (4.323-6.161-2.139) 3.07 (6.262-4.159-2.057) TCN(AT)  5.153 (6.337-5.434-3.488)  5.603(6.031-5.349-3.714) 4.383 (5.709-5.95-2.741) 4.543 (7.337-5.664-3.131)Pyramid(AT)  5.947 (6.555-6.056-5.231)  5.587 (5.841-5.444-3.333) 4.489(6.146-5.98-2.485) 4.236 (6.799-5.341-2.793) Transformer(AT)  5.589(6.57-6.352-3.223)  6.492 (6.888-6.095-4.968)  4.7 (6.146-6.884-2.561)4.605 (6.508-6.047-2.348) TCN(RT)  6.555 (6.757-6.804-4.686)  7.158(8.142-7.126-5.619) 6.854 (6.869-7.983-4.835) 7.413 (9.7-6.293-5.111)Pyramid(RT)  7.224 (7.8-7.271-5.838)  7.777 (9.682-6.714-5.714) 6.628(7.411-6.688-4.504) 7.597 (9.316-6.63-6.001)

Overall, these test results demonstrate that the systems and methodsdescribed above can be used to accurately predict an intra-aorticpressure of a patient. Advance warning of imminent changes in theintra-aortic pressure of a patient, even if the warning comes only 5 to15 minutes ahead, can greatly enhance clinical outcomes. For example,the authors of Wijnberge et al., Effect of a Machine Learning-DerivedEarly Warning System for Intraoperative Hypotension vs Standard Care onDepth and Duration of Intraoperative Hypotension During ElectiveNoncardiac Surgery: The HYPE Randomized Clinical Trial, JAMA, Caring forthe Critically Ill Patient, doi:10.1001/jama.2020.0592, 2020 observedthat significantly less time spent in hypotensive events during surgerywhen a machine learning warning system was used to inform clinicians ofpossible hypotension. Being able to forecast significant changes (e.g.,+/−10 mmHg) in intra-aortic pressure and notifying caregivers givesclinicians time to appropriately intervene before hemodynamicinstability occurs. Additionally, intra-aortic pressure forecasting canaid in weaning a patient from mechanical circulatory support followingnative heart recovery. Since the level of hemodynamic support can bevaried by altering the motor speed of the transvalvular pump, advanceforecasting of MAP can also aid in maintenance/escalation of hemodynamicsupport.

From the foregoing and with reference to the various figure drawings,those skilled in the art will appreciate that certain modifications canalso be made to the present disclosure without departing from the scopeof the same. While several implementations of the disclosure have beenshown in the drawings, it is not intended that the disclosure be limitedthereto, as it is intended that the disclosure be as broad in scope asthe art will allow and that the specification be read likewise.Therefore, the above description should not be construed as limiting,but merely as exemplifications of particular implementations. Thoseskilled in the art will envision other modifications within the scopeand spirit of the claims appended hereto.

1. A system comprising one or more processors configured to: obtain aset of intra-aortic pressure measurements corresponding to pressurevalues measured by a pressure sensor of a transvalvular micro-axialheart pump during a period of time when the transvalvular micro-axialheart pump is at least partially located in a patient's heart; obtain aset of current measurements corresponding to an energy intake of a motorof the transvalvular micro-axial heart pump during the period of time;and predict, using a trained machine learning model, an intra-aorticpressure of a patient based on the sets of intra-aortic pressure andcurrent measurements.
 2. (canceled)
 3. The system of claim 1, whereinthe transvalvular micro-axial heart pump further comprises a tube, aninlet area having one or more openings through which blood may be drawninto the tube by the motor, and an outlet area having one or moreopenings through which blood may be expelled from the tube by the motor,and wherein the pressure sensor is coupled to the outlet area.
 4. Thesystem of claim 3, wherein the transvalvular micro-axial heart pumpfurther comprises an additional pressure sensor coupled to the inletarea, wherein the one or more processors are further configured toobtain a set of left ventricular pressure measurements corresponding topressure values measured by the additional pressure sensor during theperiod of time, and wherein the prediction is further based on the setof left ventricular pressure measurements.
 5. The system of claim 1,wherein the machine learning model is a deep learning model.
 6. Thesystem of claim 5, wherein the deep learning model is an AutoregressiveIntegrated Moving Average (ARIMA) model, a Deep Neural Network (DNN)model, a Recurrent Sequence to Sequence model, a Recurrent Sequence toSequence model with Attention, a Transformer model, a TemporalConvolutional Neural Network (TCN) model, or a Convolutional NeuralPyramid model.
 7. The system of claim 5, wherein the deep learning modelis a Recurrent Sequence to Sequence model with a Legendre Memory Unit(LMU).
 8. The system of claim 1, wherein the machine learning model istrained on a data set comprising increasing sequences, decreasingsequences, and stationary sequences, and wherein each sequence comprisesintra-aortic pressure and motor speed measurements.
 9. The system ofclaim 8, wherein a sequence is increasing if the intra-aortic pressuremeasurements within that sequence increase by more than a predeterminedthreshold, wherein a sequence is decreasing if the intra-aortic pressuremeasurements within that sequence decrease by more than thepredetermined threshold, and wherein a sequence is stationary if theintra-aortic pressure measurements within that sequence do not increaseor decrease by more than the predetermined threshold.
 10. The system ofclaim 9, wherein the predetermined threshold is 10 mmHg.
 11. The systemof claim 8, wherein each sequence comprises a predetermined number ofaortic pressure and motor speed measurements.
 12. The system of claim 8,wherein each sequence comprises real-time (RT) intra-aortic pressure andmotor speed measurements.
 13. The system of claim 8, wherein eachsequence comprises average time (AT) intra-aortic pressure and motorspeed measurements.
 14. The system of claim 1, wherein the machinelearning model is trained on a data set comprising only increasing anddecreasing sequences, and wherein each sequence comprises intra-aorticpressure and motor speed measurements.
 15. The system of claimer 14,wherein a sequence is increasing if the intra-aortic pressuremeasurements within that sequence increase by more than a predeterminedthreshold, and wherein a sequence is decreasing if the intra-aorticpressure measurements within that sequence decrease by more than thepredetermined threshold.
 16. The system of claim 28, whereinautomatically adjusting the speed setting of the motor based on thepredicted intra-aortic pressure of the patient comprises temporarilyincreasing the speed setting of the motor when the predictedintra-aortic pressure of the patient is less than a current intra-aorticpressure of the patient by more than a predetermined amount.
 17. Thesystem of claim 1, further comprising: a display configured to displaythe predicted intra-aortic pressure of the patient.
 18. The system ofclaim 17, wherein the display is configured to simultaneously displaythe predicted intra-aortic pressure of the patient with a currentintra-aortic pressure of the patient and a current speed setting of themotor.
 19. The system of claim 17, wherein the display is furtherconfigured to display an alert when the predicted intra-aortic pressureof the patient is less than a current intra-aortic pressure of thepatient by more than a predetermined amount.
 20. The system of claim 17,wherein the display is configured to display the predicted intra-aorticpressure of the patient as part of a graph.
 21. A method comprising:obtaining a set of intra-aortic pressure measurements corresponding topressure values measured by a pressure sensor of a transvalvularmicro-axial heart pump during a period of time when the transvalvularmicro-axial heart pump is at least partially located in a patient'sheart; obtaining a set of current measurements corresponding to anenergy intake of a motor of the transvalvular micro-axial heart pumpduring the period of time; and predicting, using a trained machinelearning model, an intra-aortic pressure of the patient based on thesets of intra-aortic pressure and current measurements.
 22. (canceled)23. The method of claim 21 wherein the transvalvular micro-axial heartpump further comprises a tube, an inlet area having one or more openingsthrough which blood may be drawn into the tube by the motor, and anoutlet area having one or more openings through which blood may beexpelled from the tube by the motor, and wherein the pressure sensor iscoupled to the outlet area.
 24. The method of claim 23, wherein thetransvalvular micro-axial heart pump further comprises an additionalpressure sensor coupled to the inlet area, wherein the method furthercomprises obtaining a set of left ventricular pressure measurementscorresponding to pressure values measured by the additional pressuresensor during the period of time, and wherein the prediction is furtherbased on the set of left ventricular pressure measurements.
 25. Themethod of claim 21, further comprising: adjusting an amount of amedication provided to the patient based on the predicted intra-aorticpressure.
 26. The method of claim 29, further comprising: decreasing themotor speed if the intra-aortic pressure is predicted to increase. 27.The method of claim 29, further comprising: increasing the motor speedif the intra-aortic pressure is predicted to decrease.
 28. The system ofclaim 1, wherein the one or more processors are further configured toautomatically adjust a speed setting of the motor based on the predictedintra-aortic pressure of the patient.
 29. The method of claim 21,further comprising: automatically adjusting a speed setting of the motorbased on the predicted intra-aortic pressure of the patient.
 30. Anon-transitory computer readable storage medium having instructionsstored thereon that, when executed by one or more processors, cause theone or more processors to: obtain a set of intra-aortic pressuremeasurements corresponding to pressure values measured by a pressuresensor of a transvalvular micro-axial heart pump during a period of timewhen the transvalvular micro-axial heart pump is at least partiallylocated in a patient's heart; obtain a set of current measurementscorresponding to an energy intake of a motor of the transvalvularmicro-axial heart pump during the period of time; and predict, using atrained machine learning model, an intra-aortic pressure of a patientbased on the sets of intra-aortic pressure and current measurements.